Previous studies on the behavioral implications of recommender systems suggest that consumer preferences after consumption are malleable and tend to shift towards the ratings presented by a recommender system because of the anchoring effects. Drawing upon the literature on consumer satisfaction, we show that such a view on the anchoring effects of recommender systems is incomplete. Apart from the assimilation effects that pull the consumers’ preferences towards the anchor, the contrast effects may shift their preferences in the other direction. Therefore, we theoretically hypothesize that the impacts of recommendations on consumers’ constructed preferences are dependent on the level of deviation of the presented rating. The hypotheses are validated through a laboratory experiment. Our findings extend the existing literature on behavioral implications of recommender systems and provide a more comprehensive theoretical lens for understanding the anchoring effects, which may offer helpful insights for improving the design and use of recommender systems.

Sales promotion is getting more and more prosperous in Chinese cross-border e-commerce platforms where the demand is uncertain. However, most existing literature on promotion strategies is focusing on deterministic demand. In this paper, we propose a game-theoretical model under multiplicative stochastic demand to investigate the pricing, inventory quantity and sales promotion strategies for a supply chain which is consisted of one cross-border distributor and one capital-constrained retailer under a consignment contract. We obtain the equilibrium outcomes under stochastic demand, and find that the optimal price and promotion investment depend on demand uncertainty under endogenous inventory decisions. With exogenous unlimited inventory, the retailer prefers owing promotion right when the elasticity of price and promotion is small enough and its capital is sufficient, while the distributor always prefers to control sales promotion. With endogenous inventory quantity, the sensitivity of demand to price is influence by the demand uncertainty. The retailer prefers to decide the promotion when the price-elasticity is small, while the distributor prefers to decide the promotion under large promotion-elasticity. And the intensity of optimal sales promotion made by retailers may be stronger than that when the distributor owns the promotion right, which depends on the elasticity of price and promotion. More importantly, it is always better for consumers when the distributor reserves the promotion right as a lower optimal retailing price is offered.

Grain security is one of the most important issues worldwide. Many developing countries, including China, have adopted the Agriculture Support Price (ASP) program to stimulate farmers’ enthusiasm for growing grain, to ensure self-sufficiency in grain and the stable development of the grain market. To propose decision support for the government in designing a more reasonable support price in the ASP program, we formulate an agent-based model to simulate the operation of the wheat market in the harvest period. To formulate the formation process of the market price influenced by farmers’ expected sale price, processors’ expected purchase price, and the ASP, the time series and regression methods are adopted. Based on the proposed market price model, to quantitatively analyze the grain transaction process and the ASP program’s impacts on market agents, we develop an agent-based simulation model to describe the adaptive evolution and interaction among market agents. Furthermore, we validate and implement the simulation model with public wheat market data. Finally, insights and suggestions about the decision of the ASP program are provided.

There are two main issues of fuzzy multi-attribute decision-making: determine the weight of each attribute and choose an appropriate aggregation method to integrate the evaluation information of different attributes. In order to solve the multi-attribute decision-making problem in generalized orthopair fuzzy environment with unknown attribute weights more effectively, we give a decision-making method based on generalized orthopair fuzzy definite integrals. To be specific, we first introduce the complement operations of q-rung orthopair fuzzy numbers, and then investigate the multiplicative q-rung orthopair fuzzy calculus. Through the complement operations, we establish the mutual conversion formula between additive and multiplicative q-rung orthopair fuzzy calculus theory. Then, we give a multiplicative integral-based q-rung orthopair fuzzy multi-attribute decision-making method, and discuss the relationship between the q-rung orthopair fuzzy definite integrals and the q-rung orthopair fuzzy weighted geometric operator. Compared with traditional decision-making methods, this method does not rely on subjective weight information, which is especially important when dealing with large sample data. Finally, the application of election is studied to verify the feasibility and effectiveness of the proposed method. With the introduction of generalized orthopair fuzzy sets, the expression form of election evaluation information has been expanded. We also provide some examples to compare the obtained results with the results generated by the addition operation and reveal the correlation between them.

This paper deals with seru scheduling problems with multiple due windows assignment and DeJong’s learning effect. Specific time intervals are assigned to jobs with multiple due windows and learning effect is introduced to characterize the decrease of processing times with the accumulation of the working experience. We assume that the set of jobs assigned to each due window is independent, and no inclusion exists between due windows. The objective is to determine the optimal due window positions and sizes, the set of jobs assigned to each due window, and the optimal schedule in each seru to minimize a multidimensional function, which consists of the earliness and tardiness punishment cost, as well as the due window related starting time and size cost. We find that when the number of jobs and the due windows assigned to each seru are pre-specified in advance, the problem can be solved in polynomial time. Meanwhile, the impacts of the due-window allocation strategy and learning effect on the total cost are respectively discussed based on numerical examples and special cases. The results show that if each seru is assigned with the same number of due windows, the total cost can be reduced with the increasing ratio of the due-window number to the to-be-processed job number. Furthermore, with an increasing learning effect, the total cost will be decreased.

The article “Equilibrium Joining Strategies in the M/M/1 Queues with Setup Times under N-Policy” unfortunately contained a mistake about the first author’s affiliation. In the original publication of the paper, this affiliation was “Department of Mathematics, Beijing Jiaotong University, Beijing 100044, China”. It should be “School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China”.